{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[[1 2 3]\n",
      "  [1 2 4]]], shape=(1, 2, 3), dtype=int32)\n",
      "(1, 2, 3)\n",
      "tf.Tensor(\n",
      "[[1 2 3]\n",
      " [1 2 4]], shape=(2, 3), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "data=tf.constant([\n",
    "    [\n",
    "        [1,2,3],\n",
    "        [1,2,4]\n",
    "    ]\n",
    "\n",
    "])\n",
    "\n",
    "print(data)\n",
    "print(data.shape)\n",
    "print(tf.squeeze(data,axis=0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
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 "nbformat": 4,
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